Nidhi, from a small town in Bihar, joined a BA (History) programme after school. Within a few months of college, she was interested in diversifying her learning into the exciting areas of data science and artificial intelligence (AI).
Shruti from Delhi picked the biology group after Class 10. After joining a bachelor’s programme in pharmacy, she wanted to learn more mathematics, programming, and machine learning.
Shreya from Mangalore, having taken Commerce group in Class 11 and 12, went on to complete her Chartered Accountancy. Later, she wanted to shift to data science and explore connections between accounting and AI.
Can the three of them actually learn data science, machine learning, and AI? Do they have the background knowledge? Is it possible to design a data science programme that can accommodate their unique requirements and help them excel?
The answer is a resounding yes. It is possible to learn not only data science but also programming and application development ideas that add significant value to a data science degree, starting with almost any background, if one is willing to spend the time and effort. In fact, all three of them are currently enrolled in one such programme – the BS (Data Science and Applications) program of the Indian Institute of Technology Madras (IITM).
The core foundations of data science and AI come from basic concepts of mathematics, statistics, and computing. Some of the subjects that need to be covered as foundations are calculus, probability, linear algebra, discrete mathematics, statistics, estimation, inference, computational thinking, and Python programming.
Students who study mathematics and computer science in Classes 11 and 12 learn these as part of their curriculum. For others, it is possible to start learning these concepts at an introductory level even after completing their school education. Through a set of carefully designed foundational courses, these topics can be introduced from elementary principles and gently escalated to the college level within the first year of a programme.
For instance, the foundation level of IITM’s BS (data science and applications) programme covers precisely these topics spread over 8 subjects, assuming a Class 10 mathematics background and nothing else. As of now, 22,000+ learners in this BS programme are learning these fundamentals solely through these foundational courses, and more than 31% of them are from a non-engineering background.
Once the foundations have been mastered, students can proceed to pick up the essential skills expected of an application programmer and a data scientist. This requires background in additional topics in linear algebra, probabilistic modelling, and optimisation on the data science side.
On the application development side, a background is needed in data structures and algorithms, databases, and advanced programming concepts. With this background, students can start building applications through courses on application development using a standard framework in Python (for instance).
Students can further learn machine learning (ML) techniques and practice building ML models for complex data sets and business data to inform decision-making. At this point, students would be ready to do hands-on projects as they do at the end of the diploma level of IITM’s BS (Data Science and Applications) program. Having completed courses at the diploma level, students will be able to secure internships and jobs in companies in the area of data science and AI.
After mastering hands-on skills, students will be ready to take on advanced topics in AI, such as deep learning, natural language processing, and big data analysis. The knowledge gained through the foundational and skills-oriented courses should equip the students with the necessary abilities to tackle these advanced topics.
In addition to making the students industry-ready, these subjects can prepare them to pursue higher education. As data science and AI have various applications, gaining domain knowledge that can aid in using AI tools effectively to address domain-specific problems is also helpful. In fact, Shruti, who is pursuing IITM’s BS in Data Science and Applications program and her pharmacy degree, is armed with the requisite knowledge to tackle challenging problems in healthcare using AI tools.
Students will also be able to apply to institutions for masters’ programs in data science and AI based on their learning. As an example, Shreya has today gained admission to a masters in data science program in a US school based on her IITM program in data science.
The educational institutions can create programs in application development, data science and AI that are accessible to almost everyone out of school. IIT Madras, the number one ranked institution in the country according to NIRF, has led the way and shown how such a programme can be offered at low cost in hybrid mode. With more such programs being introduced by all institutions, the landscape of data science and AI learning can become truly democratic with no entry barriers based on background.
Original News Link
https://indianexpress.com/article/education/jeemain-2024-how-everyone-can-learn-artificial-intelligence-a-lesson-from-iit-9069126/
